Effect of graph similarity on recovery. The second experiment explores demixing with two Barabási-Albert graphs [1]} of varying \(\text{N}\)  (built following the scale-free model from a seeding Erdős-Rényi graph of \(\text{N}/10\)  nodes and edge probability \(0.1\) ) and \(\text{L}=3\) . The two graphs are generated so that \(\alpha \)  percent of their edges are the same. Note that \(\alpha =1\)  leads to demixing with a single diffusing graph. fig:twograph shows that, as expected, the multi-graph demixing strategy (cf. S:BlindDem) fails for \(\alpha =1\) , although the performance does not considerably drop until \(\alpha >0.9\) .
